{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "68e1c158",
   "metadata": {},
   "source": [
    "# Streaming Results\n",
    "\n",
    "Here is an example pattern if you want to stream your multiple results. Note that this is not supported for Hugging Face text completions at this time.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3dd8590",
   "metadata": {},
   "source": [
    "Import Semantic Kernel SDK from pypi.org"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a77bdf89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: if using a virtual environment, do not run this cell\n",
    "%pip install -U semantic-kernel\n",
    "from semantic_kernel import __version__\n",
    "\n",
    "__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd94029f",
   "metadata": {},
   "source": [
    "Initial configuration for the notebook to run properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7547e59b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make sure paths are correct for the imports\n",
    "\n",
    "import os\n",
    "import sys\n",
    "\n",
    "notebook_dir = os.path.abspath(\"\")\n",
    "parent_dir = os.path.dirname(notebook_dir)\n",
    "grandparent_dir = os.path.dirname(parent_dir)\n",
    "\n",
    "\n",
    "sys.path.append(grandparent_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ba03ae",
   "metadata": {},
   "source": [
    "### Configuring the Kernel\n",
    "\n",
    "Let's get started with the necessary configuration to run Semantic Kernel. For Notebooks, we require a `.env` file with the proper settings for the model you use. Create a new file named `.env` and place it in this directory. Copy the contents of the `.env.example` file from this directory and paste it into the `.env` file that you just created.\n",
    "\n",
    "**NOTE: Please make sure to include `GLOBAL_LLM_SERVICE` set to either OpenAI, AzureOpenAI, or HuggingFace in your .env file. If this setting is not included, the Service will default to AzureOpenAI.**\n",
    "\n",
    "#### Option 1: using OpenAI\n",
    "\n",
    "Add your [OpenAI Key](https://openai.com/product/) key to your `.env` file (org Id only if you have multiple orgs):\n",
    "\n",
    "```\n",
    "GLOBAL_LLM_SERVICE=\"OpenAI\"\n",
    "OPENAI_API_KEY=\"sk-...\"\n",
    "OPENAI_ORG_ID=\"\"\n",
    "OPENAI_CHAT_MODEL_ID=\"\"\n",
    "OPENAI_TEXT_MODEL_ID=\"\"\n",
    "OPENAI_EMBEDDING_MODEL_ID=\"\"\n",
    "```\n",
    "The names should match the names used in the `.env` file, as shown above.\n",
    "\n",
    "#### Option 2: using Azure OpenAI\n",
    "\n",
    "Add your [Azure Open AI Service key](https://learn.microsoft.com/azure/cognitive-services/openai/quickstart?pivots=programming-language-studio) settings to the `.env` file in the same folder:\n",
    "\n",
    "```\n",
    "GLOBAL_LLM_SERVICE=\"AzureOpenAI\"\n",
    "AZURE_OPENAI_API_KEY=\"...\"\n",
    "AZURE_OPENAI_ENDPOINT=\"https://...\"\n",
    "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=\"...\"\n",
    "AZURE_OPENAI_TEXT_DEPLOYMENT_NAME=\"...\"\n",
    "AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=\"...\"\n",
    "AZURE_OPENAI_API_VERSION=\"...\"\n",
    "```\n",
    "The names should match the names used in the `.env` file, as shown above.\n",
    "\n",
    "For more advanced configuration, please follow the steps outlined in the [setup guide](./CONFIGURING_THE_KERNEL.md)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd931c14",
   "metadata": {},
   "source": [
    "We will load our settings and get the LLM service to use for the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9a5c87a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from services import Service\n",
    "\n",
    "from samples.service_settings import ServiceSettings\n",
    "\n",
    "service_settings = ServiceSettings()\n",
    "\n",
    "# Select a service to use for this notebook (available services: OpenAI, AzureOpenAI, HuggingFace)\n",
    "selectedService = (\n",
    "    Service.AzureOpenAI\n",
    "    if service_settings.global_llm_service is None\n",
    "    else Service(service_settings.global_llm_service.lower())\n",
    ")\n",
    "print(f\"Using service type: {selectedService}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d8ddffc1",
   "metadata": {},
   "source": [
    "First, we will set up the text and chat services we will be submitting prompts to.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f8dcbc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from semantic_kernel import Kernel\n",
    "from semantic_kernel.connectors.ai.open_ai import (\n",
    "    AzureChatCompletion,\n",
    "    AzureChatPromptExecutionSettings,  # noqa: F401\n",
    "    AzureTextCompletion,\n",
    "    OpenAIChatCompletion,\n",
    "    OpenAIChatPromptExecutionSettings,  # noqa: F401\n",
    "    OpenAITextCompletion,\n",
    "    OpenAITextPromptExecutionSettings,  # noqa: F401\n",
    ")\n",
    "from semantic_kernel.contents import ChatHistory  # noqa: F401\n",
    "\n",
    "kernel = Kernel()\n",
    "\n",
    "service_id = None\n",
    "if selectedService == Service.OpenAI:\n",
    "    from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion\n",
    "\n",
    "    service_id = \"default\"\n",
    "    oai_chat_service = OpenAIChatCompletion(\n",
    "        service_id=\"oai_chat\",\n",
    "    )\n",
    "    oai_text_service = OpenAITextCompletion(\n",
    "        service_id=\"oai_text\",\n",
    "    )\n",
    "elif selectedService == Service.AzureOpenAI:\n",
    "    from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion\n",
    "\n",
    "    service_id = \"default\"\n",
    "    aoai_chat_service = AzureChatCompletion(\n",
    "        service_id=\"aoai_chat\",\n",
    "    )\n",
    "    aoai_text_service = AzureTextCompletion(\n",
    "        service_id=\"aoai_text\",\n",
    "    )\n",
    "\n",
    "# Configure Hugging Face service\n",
    "if selectedService == Service.HuggingFace:\n",
    "    from semantic_kernel.connectors.ai.hugging_face import (\n",
    "        HuggingFacePromptExecutionSettings,  # noqa: F401\n",
    "        HuggingFaceTextCompletion,\n",
    "    )\n",
    "\n",
    "    hf_text_service = HuggingFaceTextCompletion(ai_model_id=\"distilgpt2\", task=\"text-generation\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "50561d82",
   "metadata": {},
   "source": [
    "Next, we'll set up the completion request settings for text completion services.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "628c843e",
   "metadata": {},
   "outputs": [],
   "source": [
    "oai_prompt_execution_settings = OpenAITextPromptExecutionSettings(\n",
    "    service_id=\"oai_text\",\n",
    "    max_tokens=150,\n",
    "    temperature=0.7,\n",
    "    top_p=1,\n",
    "    frequency_penalty=0.5,\n",
    "    presence_penalty=0.5,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "857a9c89",
   "metadata": {},
   "source": [
    "## Streaming Open AI Text Completion\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2979db8",
   "metadata": {},
   "outputs": [],
   "source": [
    "if selectedService == Service.OpenAI:\n",
    "    prompt = \"What is the purpose of a rubber duck?\"\n",
    "    stream = oai_text_service.get_streaming_text_contents(prompt=prompt, settings=oai_prompt_execution_settings)\n",
    "    async for message in stream:\n",
    "        print(str(message[0]), end=\"\")  # end = \"\" to avoid newlines"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4288d09f",
   "metadata": {},
   "source": [
    "## Streaming Azure Open AI Text Completion\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5319f14d",
   "metadata": {},
   "outputs": [],
   "source": [
    "if selectedService == Service.AzureOpenAI:\n",
    "    prompt = \"provide me a list of possible meanings for the acronym 'ORLD'\"\n",
    "    stream = aoai_text_service.get_streaming_text_contents(prompt=prompt, settings=oai_prompt_execution_settings)\n",
    "    async for message in stream:\n",
    "        print(str(message[0]), end=\"\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "eb548f9c",
   "metadata": {},
   "source": [
    "## Streaming Hugging Face Text Completion\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be7b1c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "if selectedService == Service.HuggingFace:\n",
    "    hf_prompt_execution_settings = HuggingFacePromptExecutionSettings(\n",
    "        service_id=\"hf_text\",\n",
    "        extension_data={\n",
    "            \"max_new_tokens\": 80,\n",
    "            \"top_p\": 1,\n",
    "            \"eos_token_id\": 11,\n",
    "            \"pad_token_id\": 0,\n",
    "        },\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9525e4f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "if selectedService == Service.HuggingFace:\n",
    "    prompt = \"The purpose of a rubber duck is\"\n",
    "    stream = hf_text_service.get_streaming_text_contents(\n",
    "        prompt=prompt, prompt_execution_settings=hf_prompt_execution_settings\n",
    "    )\n",
    "    async for text in stream:\n",
    "        print(str(text[0]), end=\"\")  # end = \"\" to avoid newlines"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "da632e12",
   "metadata": {},
   "source": [
    "Here, we're setting up the settings for Chat completions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5f11e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "oai_chat_prompt_execution_settings = OpenAIChatPromptExecutionSettings(\n",
    "    service_id=\"oai_chat\",\n",
    "    max_tokens=150,\n",
    "    temperature=0.7,\n",
    "    top_p=1,\n",
    "    frequency_penalty=0.5,\n",
    "    presence_penalty=0.5,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d6bf238e",
   "metadata": {},
   "source": [
    "## Streaming OpenAI Chat Completion\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dabc6a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "if selectedService == Service.OpenAI:\n",
    "    content = \"You are an AI assistant that helps people find information.\"\n",
    "    chat = ChatHistory()\n",
    "    chat.add_system_message(content)\n",
    "    stream = oai_chat_service.get_streaming_chat_message_contents(\n",
    "        chat_history=chat, settings=oai_chat_prompt_execution_settings\n",
    "    )\n",
    "    async for text in stream:\n",
    "        print(str(text[0]), end=\"\")  # end = \"\" to avoid newlines"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cdb8f740",
   "metadata": {},
   "source": [
    "## Streaming Azure OpenAI Chat Completion\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da1e9f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "az_oai_chat_prompt_execution_settings = AzureChatPromptExecutionSettings(\n",
    "    service_id=\"aoai_chat\",\n",
    "    max_tokens=150,\n",
    "    temperature=0.7,\n",
    "    top_p=1,\n",
    "    frequency_penalty=0.5,\n",
    "    presence_penalty=0.5,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b74a64a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "if selectedService == Service.AzureOpenAI:\n",
    "    content = \"You are an AI assistant that helps people find information.\"\n",
    "    chat = ChatHistory()\n",
    "    chat.add_system_message(content)\n",
    "    chat.add_user_message(\"What is the purpose of a rubber duck?\")\n",
    "    stream = aoai_chat_service.get_streaming_chat_message_contents(\n",
    "        chat_history=chat, settings=az_oai_chat_prompt_execution_settings\n",
    "    )\n",
    "    async for text in stream:\n",
    "        print(str(text[0]), end=\"\")  # end = \"\" to avoid newlines"
   ]
  }
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